Leveraging immune resistance archetypes in solid cancer to inform next-generation anticancer therapies

Abstract

Anticancer immunotherapies, such as immune checkpoint inhibitors, bispecific antibodies, and chimeric antigen receptor T cells, have improved outcomes for patients with a variety of malignancies. However, most patients either do not initially respond or do not exhibit durable responses due to primary or adaptive/acquired immune resistance mechanisms of the tumor microenvironment. These suppressive programs are myriad, different between patients with ostensibly the same cancer type, and can harness multiple cell types to reinforce their stability. Consequently, the overall benefit of monotherapies remains limited. Cutting-edge technologies now allow for extensive tumor profiling, which can be used to define tumor cell intrinsic and extrinsic pathways of primary and/or acquired immune resistance, herein referred to as features or feature sets of immune resistance to current therapies. We propose that cancers can be characterized by immune resistance archetypes, comprised of five feature sets encompassing known immune resistance mechanisms. Archetypes of resistance may inform new therapeutic strategies that concurrently address multiple cell axes and/or suppressive mechanisms, and clinicians may consequently be able to prioritize targeted therapy combinations for individual patients to improve overall efficacy and outcomes.

Keywords: cytotoxicity, immunologic; gene expression profiling; immune evation; review; tumor microenvironment.

Figure 1

Feature sets of immune resistance. The currently understood immune resistance mechanisms operative in solid tumors can be broadly classified into five major feature sets: immune effector cell exclusion, lack of tumor antigen recognition, immune cell dysfunction/death, suppressive immune cells, and extrinsic suppressive factors. Each feature set encompasses tumor cell intrinsic and extrinsic pathways that have been characterized during primary and/or acquired immune resistance. For example, the immune effector cell exclusion feature set encompasses resistance mechanisms that lead to poor T cell infiltration of tumors, such as a lack of cGAS/STING activation, the presence of TGFβ-rich stroma, or tumor-intrinsic WNT/β-catenin signaling. The lack of tumor antigen recognition encompasses mechanisms that prevent tumor recognition by T cells, such as a paucity of tumor antigens, antigen loss, HLA or β2M deficiency, or changes in proteasome processing machinery within tumor cells. The immune cell dysfunction/death feature set includes mechanisms that drive T cell death, such as Fas ligand expression, or loss of cytolytic effector function, such as inhibitory receptor/ligand interactions and differentiation toward an exhausted state. The suppressive immune cells feature set includes cells that either prevent T cell activation or inhibit T cell function. The extrinsic suppressive factors feature set includes mechanisms that restrain antitumor immune responses, such as nutrient limitation, the presence of suppressive metabolites, or microbiota. As tumors often engage more than one immune resistance mechanism to escape killing by immune effector cells, we propose that cutting-edge technologies should be leveraged to identify the immune resistance features active in tumors, which altogether define an immune resistance archetype. Subsequently, novel therapeutics and/or combination approaches should aim to address immune resistance archetypes by addressing multiple mechanisms/feature sets concurrently. This figure was created with BioRender.com. β2M, beta-2-microglobulin. cGAS/STING, cyclic GMP-AMP synthase-stimulator of interferon genes.

Figure 2

High-dimensional technologies for immune resistance feature set characterization. Classically, the assays used to identify mechanisms of immune response/resistance have prioritized genomic sequencing, single-color or dual-color IHC staining, and flow cytometry. Expanding this characterization to include additional mechanisms of immune resistance will require implementation of cutting-edge high-dimensional technologies, such as single cell sequencing, multiplex IHC, spatial transcriptomics, microbiome characterization, patient-derived ex vivo modeling, and in situ imaging technologies. This figure was created with BioRender.com. TCR, T cell receptor.

Figure 3

Immune resistance archetypes active in example tumors. (A) Immune checkpoint blockade (ICB) Responsive Melanoma represents tumors primarily engaging resistance mechanisms that drive effector cell dysfunction to evade immune-mediated killing. (B) ICB resistant melanoma represents tumors engaging immune effector cell exclusion, lack of tumor antigen recognition, and/or immune cell dysfunction immune resistance mechanisms. Because ICB-resistant melanoma may engage one or more feature sets to evade immune responses, archetypes of immune resistance in ICB-resistant melanoma include, but are not limited to, primary immune cell desert (purple), acquired MHC-I loss (orange), high Lag-3 expression (teal), a combination of any two, or of all three (dark blue). (C) Glioblastoma represents tumors engaging immune effector cell exclusion, lack of tumor antigen recognition, and suppressive immune cells immune resistance mechanisms. (D) Microsatellite stable (MSS) colorectal cancer represents tumors engaging lack of tumor antigen recognition, suppressive immune cells, and extrinsic suppressive factors as immune resistance mechanisms. MHC-I, Major Histocompatibility Class I.

Figure 4

Implementation of immune resistance archetypes for clinical decision-making. We envision the process of implementing immune resistance archetypes for clinical decision-making as an iterative cycle starting with high-dimensional, cross-platform profiling to develop and validate of immune resistance signatures. Subsequently, rational immunotherapies that address immune resistance archetypes (rather than individual immune resistance mechanisms) would be developed and implemented for clinical evaluation. Results of these trials would be correlated with immune response or resistance and evaluated for a smaller number of informative biomarkers that could be used for clinical decision-making. This pipeline could continue to be refined as new technologies emerge, new resistance mechanisms are discovered, and novel immunotherapy technologies are developed.